Detecting and attributing changes in mean precipitation is particularly challenging due to the strong influence of natural internal variability and poor performance of climate models in its simulations. There is still lack of research on human influence on mean precipitation in China. Here we use four observational datasets and CMIP6 simulations, with percentage precipitation anomaly (PPA) as a metric, to analyze precipitation changes in China and successfully detect human activity fingerprints based on the optimal fingerprinting method. Results show an increasing trend in annual mean precipitation across most regions since the 1960s, which CMIP6 models are generally able to reproduce. Human influence on China’s mean precipitation changes is detectable and separable from natural forcings. Across different regions, anthropogenic signals are detected in three sub-climatic regions: Northwest China, Northeast China, and Tibetan Plateau. Three-signal analysis further reveals that the increase in China’s mean precipitation is primarily driven by greenhouse gas forcing.